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Simulation Photoréaliste de l'Éclairage en Synthèse d'Images Nicolas Holzschuch Habilitation à Diriger des Recherches Université Joseph Fourier Grenoble I

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Overview Motivations Previous work Contributions : –Wavelet Radiosity –Frequency Analysis –GPU-based algorithms Conclusion & future work

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Overview Motivations Previous work Contributions : –Wavelet Radiosity –Frequency Analysis –GPU-based algorithms Conclusion & future work

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Photorealistic Image Synthesis Starting from the 3D model of a scene… © C. Soler, Model by L. Boissieux.

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Photorealistic Image Synthesis …we get a picture of this virtual world © C. Soler, Model by L. Boissieux.

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Motivations Why are we doing this? –Many applications: Virtual prototyping Cultural heritage Video games Special effects –Good looking pictures –Multi-disciplinar activity (CS, physics, math)

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Motivations Why are we doing this? –Many applications: Virtual prototyping Cultural heritage Video games Special effects –Good looking pictures –Multi-disciplinar activity (CS, physics, math) Model from DaimlerChrysler / RealReflect IST project / picture by C. Soler

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Motivations Why are we doing this? –Many applications: Virtual prototyping Cultural heritage Video games Special effects –Good looking pictures –Multi-disciplinar activity (CS, physics, math) © P. Müller

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Motivations Why are we doing this? –Many applications: Virtual prototyping Cultural heritage Video games Special effects –Good looking pictures –Multi-disciplinar activity (CS, physics, math) © Valve, 2006

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Motivations Why are we doing this? –Many applications: Virtual prototyping Cultural heritage Video games Special effects –Good looking pictures –Multi-disciplinar activity (CS, physics, math) © Lucas Digital Ltd., 2002

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Motivations Why are we doing this? –Many applications: Virtual prototyping Cultural heritage Video games Special effects –Good looking pictures –Multi-disciplinar activity (CS, physics, math) Why is it difficult?

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Motivations Why are we doing this? Why is it difficult? –Interactions between light and matter Complex local effects (reflections) –Self-dependent –Long distance interactions –Differences of scale/frequency –Time/quality compromise –Blurry effects are costlier –Too many computations?

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Overview Motivations Timeline Selected contributions : –Wavelet Radiosity –Frequency Analysis –GPU-based algorithms Conclusion & future work

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Timeline

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Timeline: positions

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Timeline: research themes

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Timeline: projects and contracts

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Overview Motivations Timeline Selected contributions : –Wavelet Radiosity –Frequency Analysis –GPU-based algorithms Conclusion & future work

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Overview Motivations Timeline Selected contributions : –Wavelet Radiosity –Frequency Analysis –GPU-based algorithms Conclusion & future work

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Wavelet Radiosity: credits : iMAGIS –Under the supervision of C. Puech, F. Sillion, G.Drettakis : ISA/LORIA –Working with J.-C. Paul, L. Alonso, F. Cuny, X. Cavin, C. Winkler, H. Barthélémy, S. Petitjean… : iMAGIS/GRAVIR –Working with L. Alonso (at ISA), C. Damez, F. Sillion…

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Wavelet radiosity Extension of Hierarchical Radiosity –[Gortler et al., 1993] Uses wavelets for basis functions –Haar wavelets (pw. constant) equivalent to HR M 2, M 3 wavelets : linear, quadratic functions Higher order wavelets should work better –Better representation for smooth functions Experimental study: no, they dont! –[Wilmott & Heckbert, 1997]

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Wavelet radiosity Everyone stopped working on WR –…except us at ISA/LORIA Point is: it should really work better –So why not? Experimental studies Everyone had used a na ï ve approach: –Just changing the basis function –But higher order wavelets are costlier n 2 for each patch n 4 for each interaction

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Wavelet radiosity Na ï ve implementation doesn t work Must change the approach Adapt the algorithm: –Meshing strategy –No link storage + shooting –Refinement oracle –Visibility tests

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Why didnt it work?

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Wavelets need quad/triangular patches

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Why didnt it work? Patches are then refined The initial triangulation binds the algorithm And each patch is n 2 times costlier

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Extended mesh Keep the original mesh Adapt the radiosity algorithm [Holzschuch, Cuny, Alonso, EGWR 2000]

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Extended mesh Separation between function and geometry

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Link storage and consequences Biggest issue is memory cost –n 4 for each interaction –Solution: dont store them! –[ Stamminger, Schirmacher, Slusallek & Seidel, EG 1998] But think further: –Gathering/shooting –Gathering works better with HR –…except if you dont store the interactions We should use shooting! –…then it makes more sense not to store links!

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Higher-Order Wavelets Other changes (refinement oracle, visibility) This time it works: –Smaller memory costs –Faster computations [Cuny, Holzschuch, Alonso, EG 2000]

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Memory costs Observed memory costs for the different wavelet bases (rendering time follows similar curve) Consistent with [Willmott & Heckbert 1997]!

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Higher order wavelets II Efficient global illumination algorithm –Uses higher-order wavelets –Robust, works on real-world scenes –Memory efficient So, is everything perfect? –Actually, no. –Shadow boundaries are a big issue

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Shadow boundaries Discontinuity: the oracle keeps refining Each patch is n 2 times more costly –We lose all the ground we gained elsewhere

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Discontinuity meshing Well-known method: –Shadow boundary position is known –Cut the mesh according to these discontinuities –Refine the resulting mesh Impossible with wavelet radiosity: –Wavelets only defined over quads/triangles –…but we already have a workaround!

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DM and Higher-Order Wavelets [Holzschuch and Alonso, EGSR 2004] Discontinuities are introduced only if necessary –Use regular subdivision as much as possible Modified refinement oracle: –If refinement could have been avoided using DM –Then Subdivide patch using discontinuity –Embed the new patches inside quads –Regular subdivision over the new quads

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DM and Higher-Order Wavelets M2 wavelets, point light source, no DM [Holzschuch and Alonso, EGSR 2004]

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DM and Higher-Order Wavelets M2 wavelets, point light source, with DM [Holzschuch and Alonso, EGSR 2004]

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DM and Higher-Order Wavelets M3 wavelets, area light source, with DM [Holzschuch and Alonso, EGSR 2004]

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Wavelet Radiosity: summary Compact representation of illumination Applications: –Cooperation with Nancy School of Architecture –We tested our algorithms on real-world scenes –Great for robustness Research: –Real scientific problems came up –We had to solve them, resulting in nice papers Transfer: –The software was transferred to a start-up –VSP-Technology, headed by F. Cuny

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Wavelet radiosity: looking back Shadow boundaries is an interesting issue Without DM: –90 % of computation time for shadows With DM: –90 % of implementation time for shadows Only important for (direct lighting+directly visible) Indirect lighting can use low quality version Waste of time?

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Overview Motivations Timeline Contributions : –Wavelet Radiosity –Frequency Analysis –GPU-based algorithms Conclusion & future work

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Frequency Analysis: credits Joint research work with MIT/CSAIL At MIT/CSAIL: F. Durand, E. Chan At ARTIS/GRAVIR: C. Soler, F. Sillion Financed by an INRIA Équipe Associée [Durand, Holzschuch, Soler, Chan, Sillion, SIGGRAPH 2005]

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Illumination effects Blurry reflections: From [Ramamoorthi and Hanrahan 2001]

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Illumination effects Shadow boundaries: © U. Assarsson Point light source Area light source

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Illumination effects Indirect lighting is usually blurry: Complete lighting © C. Soler 2005.

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Illumination effects Indirect lighting is usually blurry: Indirect lighting only Direct lighting only © C. Soler 2005.

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Frequency aspects of light transport Blurriness = frequency content –Sharp variations: high frequency –Smooth variations: low frequency All effects are expressed as frequency content: –Diffuse shading: low frequency –Shadows: introduce high frequencies –Indirect lighting: tends to be low frequency

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Problem statement How does light interaction in a scene explain the frequency content? Theoretical framework: –Understand the frequency spectrum of the radiance function –From equations of light transport

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Unified framework: Spatial frequency (e.g. shadows, textures) Angular frequency (e.g. blurry highlight)

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Disclaimer: not Fourier optics We do not consider wave optics, interference, diffraction Only geometrical optics From [Hecht]

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Disclaimer: not Fourier optics We do not consider wave optics, interference, diffraction Only geometrical optics From [Hecht]

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Overview Previous work Our approach: –Local light field –Transformations on local light field Case studies: –Diffuse soft shadows –Adaptive shading sampling Conclusions and future directions

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Overview Previous work Our approach: –Local light field –Transformations on local light field Case studies: –Diffuse soft shadows –Adaptive shading sampling Conclusions and future directions

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Previous work Vast body of literature: –Light field sampling –Perceptually-based rendering –Wavelets for Computer Graphics –Irradiance caching –Photon mapping –… We focus on frequency analysis in graphics: –Light field sampling –Reflection as a convolution

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Light field sampling [Chai et al. 00, Isaksen et al. 00, Stewart et al. 03] –Light field spectrum as a function of object distance –No BRDF, occlusion ignored From [Chai et al. 2000]

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Signal processing for reflection [Ramamoorthi & Hanrahan 01, Basri & Jacobs 03] Reflection on a curved surface is a convolution Direction only From [Ramamoorthi and Hanrahan 2001]

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Overview Previous work Our approach: –Local light field –Transformations on local light field Case studies: –Diffuse soft shadows –Adaptive shading sampling Conclusions and future directions

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Our approach Light sources are input signal Interactions are filters/transforms –Transport –Visibility –BRDF –Etc.

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Our approach Light sources are input signal Interactions are filters/transforms –Transport –Visibility –BRDF –Etc. Light source signal

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Our approach Light sources are input signal Interactions are filters/transforms –Transport –Visibility –BRDF –Etc. Light source signal Transport

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Our approach Light sources are input signal Interactions are filters/transforms –Transport –Visibility –BRDF –Etc. Light source signal Signal 1 Transport

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Our approach Light sources are input signal Interactions are filters/transforms –Transport –Visibility –BRDF –Etc. Light source signal Transport Occlusion Signal 2

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Our approach Light sources are input signal Interactions are filters/transforms –Transport –Visibility –BRDF –Etc. Light source signal Transport Occlusion Signal 3 Transport

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Our approach Light sources are input signal Interactions are filters/transforms –Transport –Visibility –BRDF –Etc. Light source signal Transport Occlusion Signal 4 Transport BRDF

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Local light field 4D light field, around a central ray Central ray

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Local light field 4D light field, around a central ray We study its spectrum during transport

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Local light field 4D light field, around a central ray We study its spectrum during transport

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Local light field 4D light field, around a central ray We study its spectrum during transport

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Local light field We give explanations in 2D –Local light field is therefore 2D See SIGGRAPH paper for extension to 3D

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Local light field parameterization Space and angle space angle Central ray

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Local light field representation Density plot: Area light source Space Angle

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Local light field Fourier spectrum We are interested in the Fourier spectrum of the local light field Also represented as a density plot

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Local light field Fourier spectrum Spatial frequency Angular frequency Spectrum of an area light source:

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Basics of Fourier analysis Spectrum corresponds to blurriness: –Sharpest feature has size 1/F max Convolution theorem: –Multiplication convolution Classical spectra: –Box sinc –Dirac constant

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Overview Previous work Our approach: –Local light field –Transformations on local light field Case studies: –Diffuse soft shadows –Adaptive shading sampling Conclusions and future directions

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Overview Previous work Our approach: –Local light field –Transformations on local light field Transport Occlusion BRDF Curvature Case studies Conclusions and future directions

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Example scene Blockers Light source Receiver

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Overview Previous work Our approach: –Local light field –Transformations on local light field Transport Occlusion BRDF Curvature Case studies Conclusions and future directions

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Transport in free space Shear Space Angle

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Transport in free space Shear Space Angle Shear Spatial frequency Angular freq. Spatial frequency Angular freq.

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Transport Shear This is consistent with light field spectra [Chai et al. 00, Isaksen et al. 00] From [Chai et al. 2000]

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Overview Previous work Our approach: –Local light field –Transformations on local light field Transport Occlusion BRDF Curvature Case studies Conclusions and future directions

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Occlusion: multiplication Occlusion is a multiplication in ray space –Convolution in Fourier space Creates new spatial frequency content –Related to the spectrum of the blockers

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Overview Previous work Our approach: –Local light field –Transformations on local light field Transport Occlusion BRDF Curvature Case studies Conclusions and future directions

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Overview Previous work Our approach: –Local light field –Transformations on local light field Transport Occlusion BRDF Curvature Case studies Conclusions and future directions

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BRDF integration Outgoing light: –Integration of incoming light times BRDF Outgoing light Incoming light BRDF

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BRDF integration Ray-space: convolution –Outgoing light: convolution of incoming light and BRDF –For rotationally-invariant BRDFs Fourier domain: multiplication –Outgoing spectrum: multiplication of incoming spectrum and BRDF spectrum

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BRDF in Fourier: multiplication = BRDF is bandwidth-limiting in angle

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Example: diffuse BRDF Convolve by constant: –multiply by Dirac –Only spatial frequencies remain =

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Overview Previous work Our approach: –Local light field –Transformations on local light field Transport Occlusion BRDF Curvature Case studies Conclusions and future directions

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Curved receiver Reduce to the case of a planar surface: –Unroll the curved receiver Equivalent to changing angular content: –Linear effect on angular dimension –No effect on spatial dimension Shear in the angular dimension

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Transforms: summary Radiance/FourierEffect TransportShear OcclusionMultiplication/ConvolutionAdds spatial frequencies BRDFConvolution/MultiplicationRemoves angular frequencies CurvatureShear

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More effects in paper Cosine term (multiplication/convolution) Fresnel term (multiplication/convolution) Texture mapping (multiplication/convolution) Central incidence angle (scaling) Separable BRDF Spatially varying BRDF (semi-convolution) …and extension to 3D

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Overview Previous work Our approach: –Local light field –Transformations on local light field Case studies: –Diffuse soft shadows –Adaptive shading sampling Conclusions and future directions

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Overview Previous work Our approach: –Local light field –Transformations on local light field Case studies: –Diffuse soft shadows –Adaptive shading sampling Conclusions and future directions

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Case study: diffuse soft shadows Decreasing blockers size: –First high-frequencies increase –Then only low frequency –Non-monotonic behavior

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Diffuse soft shadows (2) Occlusion : convolution in Fourier –creates high frequencies –Blockers scaled down spectrum scaled up Fourier space v (angle) x (space) Fourier space v (angle) x (space) blocker spectrum

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Diffuse soft shadows (3) Transport after occlusion: –Spatial frequencies moved to angular dimension Diffuse reflector: –Angular frequencies are cancelled

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Diffuse soft shadows (3) Transport after occlusion: –Spatial frequencies moved to angular dimension Diffuse reflector: –Angular frequencies are cancelled

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Frequency Analysis: summary Framework for frequency analysis of light transport Gives high frequencies locations Occlusion creates high frequencies Transport + reflection removes HFs High frequencies mostly if: –Small transport (quasi-contact) –Specular reflection (or highly glossy) –Point light source

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Overview Motivations Timeline Contributions : –Wavelet Radiosity –Frequency Analysis –GPU-based algorithms Conclusion & future work

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GPU-based algorithms: credits Specular reflections: D. Roger Real-time soft shadows: L. Atty, M. Lapierre, C. Hansen, J.-M. Hasenfratz, F. Sillion Contact shadows: M. Malmer, F. Malmer, U. Assarsson Global Illumination: J. Kontkannen, E. Turquin, F. Sillion

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GPU-based algorithms GPUs are highly powerful machines –Widely available –Used in the industry Video games, cinema But they also have their limitations –Memory accesses, per-pixel computations… They work better on local effects –Direct lighting, shadow boundaries… 2003: AS CNRS Real-Time Rendering

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Looking back… Radiosity: –Lots of work for direct lighting+directly visible High frequency effects in illumination: –Small transport –Shadow boundaries –Specular reflections –…mostly local or semi-local effects Involve a small subset of the scene for each pixel

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Think globally Our hard points: –Direct lighting+directly visible –Local or semi-local effects … all correspond to the forte of GPUs Main idea: –use GPUs for HF effects+directly visible –Something else (CPU) for low frequency effects

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GPU-based algorithms Using GPUs for local/semi-local effects: –Specular reflections –Shadows (soft and hard) –Contact shadows Global illumination in static scenes: –GPU for direct lighting, CPU for indirect

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Specular reflections Vertex based reflections For each vertex in the scene –Compute its reflected position –Find light paths of extremal length –Using gradient descent [Roger and Holzschuch, EG 2006]

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Specular reflections Our methodEnv. mapping Reference [Roger and Holzschuch, EG 2006]

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Real-time soft shadows Shadows: important issue for realism Real-time hard shadows: easy with GPU Soft shadows: still an open problem EG 2003: State-of-the-Art Report –Hasenfratz, Lapierre, Holzschuch, Sillion

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Soft Shadow Maps Discretize occluders using shadow map Compute soft shadow for each surfel –Easy because surfel axis-aligned with LS Add the contributions of each surfel [Atty, Holzschuch, Lapierre, Hasenfratz, Hansen, Sillion, CGF 2006]

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Contact shadows Shadows cast on nearby objects: [Malmer, Malmer, Assarsson, Holzschuch, JGT 2007]

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Contact shadows Equivalent to ambient occlusion –Important for realism –Low-cost effect, but with high impact Low frequency effect Several techniques for storing AO field –Moving with the object –Casting shadows on close objects –Indexed by direction: k*n 2 storage –Post-processing to extract AO values

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Contact shadows Our solution:

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Contact shadows Store raw AO values, un-processed 3D grid: n 3 storage No post-processing required Why is it interesting? –Low frequency effect: small values of n –Raw values stored: no constant –Similar memory costs, faster rendering

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Global Illumination in static scenes Direct lighting using GPU Indirect lighting: –Wavelet representation –precomputed Global Transport Operator [Kontkanen, Turquin, Holzschuch, Sillion, EGSR 2006]

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Global Illumination in static scenes Wavelet basis for radiance Local Transport Operator –expressing a single light bounce –precomputed Global Transport Operator –Built from LTO, using Neumann series For each frame: –Compute direct lighting, projected on WT basis –Apply GTO to direct lighting –Result is indirect lighting

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Global Illumination in static scenes DirectIndirect Global

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GPU-based algorithms: summary GPU techniques for local/semi-local effects: –Specular reflections –Soft shadows –Contact shadows Interactive global illumination –GPU for direct lighting, CPU for indirect –Limited to static scenes

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Overview Motivations Previous work Contributions : –Wavelet Radiosity –Frequency Analysis –GPU-based algorithms Conclusion & future work

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Photorealistic Image Synthesis Contributions: –Wavelet radiosity –Frequency analysis –GPU-based algorithms Several applications and contracts: –VSP-Tech, SIMULGEN, Eden Games…

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Future work: Real Time Global Illumination Frequency analysis in real-time –Used to guide computations GPU for local and semi-local HF effects –Direct lighting, shadows –Specular/glossy reflections –Occlusion and reflection fields CPU for other effects –Indirect lighting –Low Frequency effects Lots of applications and contacts: –Video games (GENAC II) –Movies

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Future work: Image-Based Rendering We want more realistic pictures Requires more realistic models –Hard to create from scratch –Image-Based Modeling for acquisition Opens several new directions of research: –Easy model acquisition –Real-time rendering of the model –Re-lighting of the model Need for cooperation: –2 ANR projects submitted this year Lots of potential applications

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Thank you!

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Acknowledgements Everyone I worked with In addition to those already mentioned: –iMAGIS, : A. Lamouret, F. Faure, JC Lombardo, JD Gascuel, N. Tsingos, M. Desbrun, S. Rivière, R. Orti, JC Vedel, A. Verroust, MP Cani, A. Opalach,… –UCT, : E. Blake, S. Nirenstein, D. Cook, A. Secchia,… –ISA, : S. Lazard, S. Merzouk, K. Tombre, H. Everett, MO Berger, A. Tabbone… –iMAGIS, : J. Thollot, X. Decoret, J. Turbet, F. Neyret, L. Reveret, G. Debunne, S. Grabli… –ARTIS, : A. Martinet, L. Boissieux, V. Ostromoukhov, E. Eisemann, P. Barla, L. Baboud, H. Bezerra, A. Bousseau, A. Orzan, T. Stein, K. Smith, F. Moulin, D. Vanderhaeghe…

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